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Predictive modeling using sparse logistic regression with applications

Research output: Book/ReportDoctoral thesisMonograph

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Predictive modeling using sparse logistic regression with applications. / Manninen, Tapio.

Tampere : Tampere University of Technology, 2014. 97 p. (Tampere University of Techology. Publication; Vol. 1190).

Research output: Book/ReportDoctoral thesisMonograph

Harvard

Manninen, T 2014, Predictive modeling using sparse logistic regression with applications. Tampere University of Techology. Publication, vol. 1190, Tampere University of Technology, Tampere.

APA

Manninen, T. (2014). Predictive modeling using sparse logistic regression with applications. (Tampere University of Techology. Publication; Vol. 1190). Tampere: Tampere University of Technology.

Vancouver

Manninen T. Predictive modeling using sparse logistic regression with applications. Tampere: Tampere University of Technology, 2014. 97 p. (Tampere University of Techology. Publication).

Author

Manninen, Tapio. / Predictive modeling using sparse logistic regression with applications. Tampere : Tampere University of Technology, 2014. 97 p. (Tampere University of Techology. Publication).

Bibtex - Download

@book{000876f53eb04e83a1e982abf4209b56,
title = "Predictive modeling using sparse logistic regression with applications",
abstract = "In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data classification. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classification method such as logistic regression often outperforms more sophisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of L1 regularized parameter estimation have a significant role in this thesis. It is shown that a combination of a careful model assessment scheme and automatic feature selection by means of logistic regression model and coefficient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classification.",
author = "Tapio Manninen",
note = "Awarding institution:Tampere University of Technology",
year = "2014",
month = "1",
day = "31",
language = "English",
isbn = "978-952-15-3226-9",
series = "Tampere University of Techology. Publication",
publisher = "Tampere University of Technology",

}

RIS (suitable for import to EndNote) - Download

TY - BOOK

T1 - Predictive modeling using sparse logistic regression with applications

AU - Manninen, Tapio

N1 - Awarding institution:Tampere University of Technology

PY - 2014/1/31

Y1 - 2014/1/31

N2 - In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data classification. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classification method such as logistic regression often outperforms more sophisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of L1 regularized parameter estimation have a significant role in this thesis. It is shown that a combination of a careful model assessment scheme and automatic feature selection by means of logistic regression model and coefficient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classification.

AB - In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data classification. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classification method such as logistic regression often outperforms more sophisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of L1 regularized parameter estimation have a significant role in this thesis. It is shown that a combination of a careful model assessment scheme and automatic feature selection by means of logistic regression model and coefficient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classification.

M3 - Doctoral thesis

SN - 978-952-15-3226-9

T3 - Tampere University of Techology. Publication

BT - Predictive modeling using sparse logistic regression with applications

PB - Tampere University of Technology

CY - Tampere

ER -